However, our experiments showed that convolutional architectures
are not limited in this way, and are capable of generalising
to test data that shares no features with the training data.
Moreover, the underlying principles of this approach arose
from models of visual cortex [1]},
and have become standard components in image processing
networks [2]}, [3]}, [4]}.
In concrete terms, weight sharing allows what is learned about
features present in the training data to be transferred to
unseen features in the test data.
